Reliability analysis of body sensor networks with correlated isolation groups

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.